Establishing Smartphone User Behavior Model Based on Energy Consumption Data

2021 ◽  
Vol 16 (2) ◽  
pp. 1-40
Author(s):  
Ming Ding ◽  
Tianyu Wang ◽  
Xudong Wang
Keyword(s):  
Energy Consumption ◽  
Large Scale ◽  
Feature Vector ◽  
User Behavior ◽  
Lower Layer ◽  
Training Data ◽  
Usage Pattern ◽  
User Behaviors ◽  
Consumption Data ◽  
Usage Patterns

In smartphone data analysis, both energy consumption modeling and user behavior mining have been explored extensively, but the relationship between energy consumption and user behavior has been rarely studied. Such a relationship is explored over large-scale users in this article. Based on energy consumption data, where each users’ feature vector is represented by energy breakdown on hardware components of different apps, User Behavior Models (UBM) are established to capture user behavior patterns (i.e., app preference, usage time). The challenge lies in the high diversity of user behaviors (i.e., massive apps and usage ways), which leads to high dimension and dispersion of data. To overcome the challenge, three mechanisms are designed. First, to reduce the dimension, apps are ranked with the top ones identified as typical apps to represent all. Second, the dispersion is reduced by scaling each users’ feature vector with typical apps to unit ℓ 1 norm. The scaled vector becomes Usage Pattern, while the ℓ 1 norm of vector before scaling is treated as Usage Intensity. Third, the usage pattern is analyzed with a two-layer clustering approach to further reduce data dispersion. In the upper layer, each typical app is studied across its users with respect to hardware components to identify Typical Hardware Usage Patterns (THUP). In the lower layer, users are studied with respect to these THUPs to identify Typical App Usage Patterns (TAUP). The analytical results of these two layers are consolidated into Usage Pattern Models (UPM), and UBMs are finally established by a union of UPMs and Usage Intensity Distributions (UID). By carrying out experiments on energy consumption data from 18,308 distinct users over 10 days, 33 UBMs are extracted from training data. With the test data, it is proven that these UBMs cover 94% user behaviors and achieve up to 20% improvement in accuracy of energy representation, as compared with the baseline method, PCA. Besides, potential applications and implications of these UBMs are illustrated for smartphone manufacturers, app developers, network providers, and so on.

scholarly journals Interaction and Engagement with an Anxiety Management App: Analysis Using Large-Scale Behavioral Data

2018 ◽  
Vol 5 (4) ◽  
pp. e58 ◽  
Author(s):  
Paul Matthews ◽  
Phil Topham ◽  
Praminda Caleb-Solly
Keyword(s):  
Mobile Phone ◽  
Social Participation ◽  
Large Scale ◽  
Cloud Services ◽  
Anxiety Reduction ◽  
Behavioral Engagement ◽  
Anxiety Management ◽  
User Behaviors ◽  
Self Help ◽  
Usage Patterns

BackgroundSAM (Self-help for Anxiety Management) is a mobile phone app that provides self-help for anxiety management. Launched in 2013, the app has achieved over one million downloads on the iOS and Android platform app stores. Key features of the app are anxiety monitoring, self-help techniques, and social support via a mobile forum (“the Social Cloud”). This paper presents unique insights into eMental health app usage patterns and explores user behaviors and usage of self-help techniques.ObjectiveThe objective of our study was to investigate behavioral engagement and to establish discernible usage patterns of the app linked to the features of anxiety monitoring, ratings of self-help techniques, and social participation.MethodsWe use data mining techniques on aggregate data obtained from 105,380 registered users of the app’s cloud services.ResultsEngagement generally conformed to common mobile participation patterns with an inverted pyramid or “funnel” of engagement of increasing intensity. We further identified 4 distinct groups of behavioral engagement differentiated by levels of activity in anxiety monitoring and social feature usage. Anxiety levels among all monitoring users were markedly reduced in the first few days of usage with some bounce back effect thereafter. A small group of users demonstrated long-term anxiety reduction (using a robust measure), typically monitored for 12-110 days, with 10-30 discrete updates and showed low levels of social participation.ConclusionsThe data supported our expectation of different usage patterns, given flexible user journeys, and varying commitment in an unstructured mobile phone usage setting. We nevertheless show an aggregate trend of reduction in self-reported anxiety across all minimally-engaged users, while noting that due to the anonymized dataset, we did not have information on users also enrolled in therapy or other intervention while using the app. We find several commonalities between these app-based behavioral patterns and traditional therapy engagement.

scholarly journals Accelerating recommendation system training by leveraging popular choices

2021 ◽  
Vol 15 (1) ◽  
pp. 127-140
Author(s):  
Muhammad Adnan ◽  
Yassaman Ebrahimzadeh Maboud ◽  
Divya Mahajan ◽  
Prashant J. Nair
Keyword(s):  
Neural Networks ◽  
Large Scale ◽  
Recommendation System ◽  
Training Data ◽  
Categorical Variables ◽  
Numerical Representation ◽  
Data Layout ◽  
Production Scale ◽  
Training Time ◽  
Usage Patterns

Recommender models are commonly used to suggest relevant items to a user for e-commerce and online advertisement-based applications. These models use massive embedding tables to store numerical representation of items' and users' categorical variables (memory intensive) and employ neural networks (compute intensive) to generate final recommendations. Training these large-scale recommendation models is evolving to require increasing data and compute resources. The highly parallel neural networks portion of these models can benefit from GPU acceleration however, large embedding tables often cannot fit in the limited-capacity GPU device memory. Hence, this paper deep dives into the semantics of training data and obtains insights about the feature access, transfer, and usage patterns of these models. We observe that, due to the popularity of certain inputs, the accesses to the embeddings are highly skewed with a few embedding entries being accessed up to 10000X more. This paper leverages this asymmetrical access pattern to offer a framework, called FAE, and proposes a hot-embedding aware data layout for training recommender models. This layout utilizes the scarce GPU memory for storing the highly accessed embeddings, thus reduces the data transfers from CPU to GPU. At the same time, FAE engages the GPU to accelerate the executions of these hot embedding entries. Experiments on production-scale recommendation models with real datasets show that FAE reduces the overall training time by 2.3X and 1.52X in comparison to XDL CPU-only and XDL CPU-GPU execution while maintaining baseline accuracy.

scholarly journals Modeling the Total Energy Consumption of Mobile Network Services and Applications

Energies ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 184 ◽  
Author(s):  
Ming Yan ◽  
Chien Aun Chan ◽  
André F. Gygax ◽  
Jinyao Yan ◽  
Leith Campbell ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Energy Consumption ◽  
User Behavior ◽  
Instant Messaging ◽  
Specific Energy Consumption ◽  
Mobile Network ◽  
Network Services ◽  
Total Energy Consumption ◽  
Usage Patterns ◽  
End To End

Reducing the energy consumption of Internet services requires knowledge about the specific traffic and energy consumption characteristics, as well as the associated end-to-end topology and the energy consumption of each network segment. Here, we propose a shift from segment-specific to service-specific end-to-end energy-efficiency modeling to align engineering with activity-based accounting principles. We use the model to assess a range of the most popular instant messaging and video play applications to emerging augmented reality and virtual reality applications. We demonstrate how measurements can be conducted and used in service-specific end-to-end energy consumption assessments. Since the energy consumption is dependent on user behavior, we then conduct a sensitivity analysis on different usage patterns and identify the root causes of service-specific energy consumption. Our main findings show that smartphones are the main energy consumers for web browsing and instant messaging applications, whereas the LTE wireless network is the main consumer for heavy data applications such as video play, video chat and virtual reality applications. By using small cell offloading and mobile edge caching, our results show that the energy consumption of popular and emerging applications could potentially be reduced by over 80%.

Energy Consumption Simulation of the Air Conditioning System in Large-Scale Buildings

2013 ◽  
Vol 724-725 ◽  
pp. 1506-1509
Author(s):  
Guang Ming Zhang ◽  
Xue Shen ◽  
Gui Zhong Tang
Keyword(s):  
Energy Consumption ◽  
Energy Demand ◽  
Air Conditioning ◽  
Large Scale ◽  
Working Environment ◽  
Operation Condition ◽  
Air Conditioning System ◽  
Factors Affecting ◽  
Network Training ◽  
Consumption Data

The working environment of air conditioning system in large-scale building is very complex, and there is no significant linear relationship between factors affecting energy consumption and energy demand of air conditioning system. This study adopts a nonlinear regression model: ANN (artificial neural network) model as energy model of air conditioning system. Take outdoor temperature, categorical day-of-week variable, equipment efficiency and terminal load as input, energy demand as output. Use energy consumption data in 2011 for network training, and energy consumption data in 2012 to verify the reliability of model. Based on energy analysis, the operation condition and the characteristics of energy consumption of air conditioning system for large-scale buildings in Nanjing could be precisely represented.

Research on Integration Technology of Large-Scale Public Building Energy Detection System Based on OPC

2012 ◽  
Vol 182-183 ◽  
pp. 950-954
Author(s):  
Chang Tao Wang ◽  
Jing Hai Zhou ◽  
Zhong Hua Han
Keyword(s):  
Energy Consumption ◽  
Large Scale ◽  
Detection System ◽  
Energy Detection ◽  
Building Energy ◽  
Development Work ◽  
Public Building ◽  
Consumption Data ◽  
Opc Technology ◽  
Standard Interface

As developing and integrating energy consumption detection system become more and more difficult, OPC technology is used to simplify the system. The integration of system can be improved largely and the development work can be decreased through OPC standard interface. Firstly, this paper introduces relative knowledge about OPC technology, and then realizes hardware and software development of energy consumption detection system in Shenyang large-scale public buildings. The result indicates that OPC can simplify system greatly and energy consumption data can be detected at real-time.

scholarly journals Analyzing Spatial and Temporal User Behavior in Participatory Sensing

2018 ◽  
Vol 7 (9) ◽  
pp. 344 ◽  
Author(s):  
Ngo Khoi ◽  
Sven Casteleyn
Keyword(s):  
Large Scale ◽  
User Behavior ◽  
High Accuracy ◽  
Participatory Sensing ◽  
Design Data ◽  
User Behaviors ◽  
Sensing Applications ◽  
Active Data ◽  
Geographical Context ◽  
Do So

The large number of mobile devices and their increasingly powerful computing and sensing capabilities have enabled the participatory sensing concept. Participatory sensing applications are now able to effectively collect a variety of information types with high accuracy. Success, nevertheless, depends largely on the active participation of the users. In this article, we seek to understand spatial and temporal user behaviors in participatory sensing. To do so, we conduct a large-scale deployment of Citizense, a multi-purpose participatory sensing framework, in which 359 participants of demographically different backgrounds were simultaneously exposed to 44 participatory sensing campaigns of various types and contents. This deployment has successfully gathered various types of urban information and at the same time portrayed the participants’ different spatial, temporal and behavioral patterns. From this deployment, we can conclude that (i) the Citizense framework can effectively help participants to design data collecting processes and collect the required data, (ii) data collectors primarily contribute in their free time during the working week; much fewer submissions are done during the weekend, (iii) the decision to respond and complete a particular participatory sensing campaign seems to be correlated to the campaign’s geographical context and/or the recency of the data collectors’ activities, and (iv) data collectors can be divided into two groups according to their behaviors: a smaller group of active data collectors who frequently perform participatory sensing activities and a larger group of regular data collectors who exhibit more intermittent behaviors. These identified user behaviors open avenues to improve the design and operation of future participatory sensing applications.

scholarly journals A Cluster-Based Routing Protocol and Fault Detection for Wireless Sensor Network

2021 ◽  
Vol 13 (04) ◽  
pp. 71-83
Author(s):  
Slaheddine Chelbi ◽  
Riadh Moussi
Keyword(s):  
Energy Consumption ◽  
Network Lifetime ◽  
Routing Protocol ◽  
Wireless Sensors ◽  
Large Scale ◽  
Routing Algorithm ◽  
Data Accuracy ◽  
Wireless Sensor ◽  
Consumption Data ◽  
Sensor Devices

In Wireless Sensors Networks (WSN) based application, a large number of sensor devices must be deployed. Energy efficiency and network lifetime are the two most challenging issues in WSN. As a consequence, the main goal is to reduce the overall energy consumption using clustering protocols which have to ensure reliability and connectivity in large-scale WSN. This work presents a new clustering and routing algorithm based on the properties of the sensor networks. The main goal of this work is to extend the network lifetime via charge equilibration in the WSN. According to many errors with sensing devices and to have greater data accuracy, we use a quorum mechanism. The proposed algorithms are evaluated widely and the results are compared with related works. The experimental results show that the proposed algorithm provides an effective improvement in terms of energy consumption, data accuracy and network lifetime.

scholarly journals Energy audit and conservation potential analysis of a large comprehensive commercial building

2018 ◽  
Vol 22 (Suppl. 2) ◽  
pp. 567-576
Author(s):  
Chunzhi Zhang ◽  
Nianxia Yuan ◽  
Qianjun Mao
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Emission Reduction ◽  
Large Scale ◽  
Rapid Development ◽  
Recovery Period ◽  
Commercial Building ◽  
Energy Saving Potential ◽  
Consumption Data ◽  
Actual Energy Consumption

With the rapid development of large-scale public buildings, energy consumption has increased, of which the energy consumption of comprehensive commercial buildings can reach 10~20 times the common building energy consumption, and has great energy saving potential. In this paper, a large comprehensive commercial building in Chengdu is taken as an example to analyze the energy consumption through the actual energy consumption data, viewed from the energy-saving and emission-reduction and static investment payback period point. The results show that the energy saving rate of the building can be achieved by 32.64%, the emission reduction is 6196.52 t CO2 per year, and the investment recovery period is only about 0.90 years, which provides a reference for similar buildings.

scholarly journals A Framework for the Automatic Integration and Diagnosis of Building Energy Consumption Data

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1395
Author(s):  
Shuang Yuan ◽  
Zhen-Zhong Hu ◽  
Jia-Rui Lin ◽  
Yun-Yi Zhang
Keyword(s):  
Energy Consumption ◽  
Large Scale ◽  
Building Energy ◽  
Primary Energy ◽  
Human Society ◽  
Building Energy Consumption ◽  
Unified Framework ◽  
Information Models ◽  
Consumption Data ◽  
Building Information

Buildings account for a majority of the primary energy consumption of the human society, therefore, analyses of building energy consumption monitoring data are of significance to the discovery of anomalous energy usage patterns, saving of building utility expenditures, and contribution to the greater environmental protection effort. This paper presents a unified framework for the automatic extraction and integration of building energy consumption data from heterogeneous building management systems, along with building static data from building information models to serve analysis applications. This paper also proposes a diagnosis framework based on density-based clustering and artificial neural network regression using the integrated data to identify anomalous energy usages. The framework and the methods have been implemented and validated from data collected from a multitude of large-scale public buildings across China.

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